Circuit breaker is an important interrupting device in power system network. It usually has a lifetime about 20 to 40 years. During breaker's service time, maintenance and inspection are imperative duties to achieve its reliable operation. To automate the diagnostic practice for circuit breaker operation and reduce the utility company's workload, Wavelet based analysis software of circuit breaker operation is developed here. Combined with circuit breaker monitoring system, the analysis software processes the original circuit breaker information, speeds up the analysis time and provides stable and consistent evaluation for the circuit breaker operation.

¡§Mura¡¨ is a local lightness variation without a clear contour on a uniform surface image, which imparts an unpleasant sensation to human vision. In this study, a Haar wavelet transform (WT) method is proposed to detect the mura of the polarizer. Because of the WT capability of multi-resolution analysis for digital images, the difference of gray level between neighbor pixels in different scale of an image can be analyzed by the WT high frequency coefficient. As a result, different size muras at different location can be segmented. Because the Haar WT cannot extract all the high frequency coefficient from an image at one time, the original image is decomposed into an main image and a subimage at the beginning. The applying the WT technique to extract all the high frequency coefficients from these two images. There exist three types of mura consisting of line mura, band mura, and area mura. Experiments were extensively conducted on different frequencies and sizes of these muras. Experimental results show that the presented approach is able to detect all the line muras, but some band and area muras. The result of this study can be extended to the future researches regarding mura properties and detection methods.

Using ultrasonic guided waves improve a problem of time-consuming and laborious with conventional ultrasonic method involving point¡Vby-point inspection. In order to measure of hundreds meters of pipeline in oil and chemical industries, guided waves inspection technique is used and developed widely. For fast and long range inspection, a pulse-echo system is used to excite Lamb waves propagating along the pipe. The collection and analysis of the returning echoes indicate the present of corrosion. But if the pipe features, like bends or supports, are corrosive, the signal of corrosions is often covered with echoes of pipe features. Then it causes the inspection more difficult. In this study, the pipe feature which discussed is the welded support. The focus of the advanced analysis, Discrete Wavelet Transform, of the echoes reflected from the welded support on a 3 inch pipe by experimental and finite element method, so as to improve the ability of corrosion inspection on pipe features. To study the feasibility of the improvement in the effect of coherent noise for guided wave inspection by Wavelet analysis. Results show that original signals can not be differentiated by comparing with signals of normal support, general corrosive support, and support with notch. But after processing these three signals by Wavelet analysis, the situation of the 3 type supports from the signals can be differentiated. The simulated results of two different models and five exciting frequencies show the similar trend to the experimental results after Wavelet transform processing. It is success on separating the signals of normal support and corrosive support with Wavelet Analysis, and this method of this study is useful to improve the effect of coherency noise for guided wave inspection.

Die Diplomarbeit beschäftigt sich mit der Detektion von Gesichtern in Bildern. Ausgehend von einem Überblick über bestehende Verfahren wird ein viel versprechendes Verfahren ausgewählt, vorgestellt und basierend auf neuen Erkenntnissen weiterentwickelt. Bilddaten aus dem Universitätsarchiv werden exemplarisch für die Evaluierung des Verfahrens verwendet.

Zugl.: Marburg, University, Diss., 2009.

This thesis consists of two topics: statistical selection and profile monitoring. Statistical selection is related to ranking and selection in simulation and profile monitoring is related to statistical process control. Ranking and selection (R&S) is to select a system with the largest or smallest performance measure among a finite number of simulated alternatives with some guarantee about correctness. Fully sequential procedures have been shown to be efficient, but their actual probabilities of correct selection tend to be higher than the nominal level, implying that they consume unnecessary observations. In the first part, we study three conservativeness sources in fully sequential indifference-zone (IZ) procedures and use experiments to quantify the impact of each source in terms of the number of observations, followed by an asymptotic analysis on the impact of the critical one. Then we propose new asymptotically valid procedures that lessen the critical conservativeness source, by mean update with or without variance update. Experimental results showed that new procedures achieved meaningful improvement on the efficiency. The second part is developing a wavelet-based distribution-free tabular CUSUM chart based on adaptive thresholding. WDFTCa is designed for rapidly detecting shifts in the mean of a high-dimensional profile whose noise components have a continuous nonsingular multivariate distribution. First computing a discrete wavelet transform of the noise vectors for randomly sampled Phase I (in-control) profiles, WDFTCa uses a matrix-regularization method to estimate the covariance matrix of the wavelet-transformed noise vectors; then those vectors are aggregated (batched) so that the nonoverlapping batch means of the wavelet-transformed noise vectors have manageable covariances. Lower and upper in-control thresholds are computed for the resulting batch means of the wavelet-transformed noise vectors using the associated marginal Cornish-Fisher expansions that have been suitably adjusted for between-component correlations. From the thresholded batch means of the wavelet-transformed noise vectors, Hotelling’s T^2-type statistics are computed to set the parameters of a CUSUM procedure. To monitor shifts in the mean profile during Phase II (regular) operation, WDFTCa computes a similar Hotelling’s T^2-type statistic from successive thresholded batch means of the wavelet-transformed noise vectors using the in-control thresholds; then WDFTCa applies the CUSUM procedure to the resulting T^2-type statistics. Experimentation with several normal and nonnormal test processes revealed that WDFTCa outperformed existing nonadaptive profile-monitoring schemes.

Wrinkling caused in wearing and laundry procedures is one of the most important performance properties of a fabric. Visual examination performed by trained experts is a routine wrinkle evaluation method in textile industry, however, this subjective evaluation is time-consuming. The need for objective, automatic and efficient methods of wrinkle evaluation has been increasing remarkably in recent years. In the present thesis, a wavelet transform based imaging analysis method was developed to measure the 2D fabric surface data captured by an infrared imaging system. After decomposing the fabric image by the Haar wavelet transform algorithm, five parameters were defined based on modified wavelet coefficients to describe wrinkling features, such as orientation, hardness, density and contrast. The wrinkle parameters provide useful information for textile, appliance, and detergent manufactures who study wrinkling behaviors of fabrics. A Support-Vector-Machine based classification scheme was developed for automatic wrinkle rating. Both linear kernel and radial-basis-function (RBF) kernel functions were used to achieve a higher rating accuracy. The effectiveness of this evaluation method was tested by 300 images of five selected fabric types with different fiber contents, weave structures, colors and laundering cycles. The results show agreement between the proposed wavelet-based automatic assessment and experts’ visual ratings. / text

We present a new method to stabilize the three-term AVO inversion using Curvelet and Wavelet transforms. Curvelets are basis functions that effectively represent otherwise smooth objects having discontinuities along smooth curves. The applied formalism explores them to make the most of the continuity along reflectors in seismic images. Combined with Wavelets, Curvelets are used to denoise the data by penalizing high frequencies and small contributions in the AVO-cube. This approach is based on the idea that rapid amplitude changes along the ray-parameter axis are most likely due to noise. The AVO-inverse problem is linearized, formulated and solved for all (x, z) at once. Using densities and velocities of the Marmousi model to define the fluctuations in the elastic properties, the performance of the proposed method is studied and compared with the smoothing along the ray-parameter direction only. We show that our method better approximates the true data after the denoising step, especially when noise level increases.

Hydraulic systems are complex dynamical systems whose performance can be degraded by certain faults, specifically internal or external leakage. The objective of this research is to develop an appropriate signal processing approach for detection and isolation of these faults. By analyzing the dynamics of the hydraulic actuator, an internal leakage is shown to increase the damping characteristic of the system and change the transient response of the pressure signals. An external leakage, on the other hand, drops the pressure signals without having a significant effect on transient responses. Offline detection of internal leakage in hydraulic actuators is first examined by using fast Fourier, wavelet and Hilbert-Huang transforms. The original pressure signal is decomposed using these transform methods and the frequency component which is sensitive to the internal leakage is identified. The root mean square of the processed pressure signal is used and a comparison of the three transforms is made to assess their ability to detect internal leakage fault, through extensive validation tests. The wavelet transform method is shown to be more suitable for internal leakage detection compared to the other two methods. The wavelet based approach is then extended to an online detection method of internal leakage fault. The online approach considers the more realistic case of an actuator that is driven in a closed-loop mode to track pseudorandom position reference inputs against a load emulated by a spring. Furthermore, the method is shown to remain effective even with control systems which are tolerant to leakage faults. Next, the application of wavelet transform to detect external leakage fault using both offline and online applications in hydraulic actuators is described. The method also examines the isolation of this fault from actuator internal leakage in a multiple-fault environment. The results show that wavelet transform is a fast and easily-implementable method for leakage detection in hydraulic actuators without any need to explicitly incorporate the model of actuator or leakage. Internal leakages as low as 0.124 lit/min, are shown to be detectable, for 80% of the times using structured input signal. For online application, internal leakages in the range of 0.2-0.25 lit/min can be identified. External leakages as low as 0.3 lit/min can be detected in all offline and online applications. Other methods such as observer based and Kalman filter methods, which require the model of the actuator or leakage fault, cannot report leakage detection of magnitudes as low as that reported in this work. The low leak rate detection and not requiring a model of the actuator or leakage make this method very attractive for industrial implementation.

Hydraulic systems are complex dynamical systems whose performance can be degraded by certain faults, specifically internal or external leakage. The objective of this research is to develop an appropriate signal processing approach for detection and isolation of these faults. By analyzing the dynamics of the hydraulic actuator, an internal leakage is shown to increase the damping characteristic of the system and change the transient response of the pressure signals. An external leakage, on the other hand, drops the pressure signals without having a significant effect on transient responses. Offline detection of internal leakage in hydraulic actuators is first examined by using fast Fourier, wavelet and Hilbert-Huang transforms. The original pressure signal is decomposed using these transform methods and the frequency component which is sensitive to the internal leakage is identified. The root mean square of the processed pressure signal is used and a comparison of the three transforms is made to assess their ability to detect internal leakage fault, through extensive validation tests. The wavelet transform method is shown to be more suitable for internal leakage detection compared to the other two methods. The wavelet based approach is then extended to an online detection method of internal leakage fault. The online approach considers the more realistic case of an actuator that is driven in a closed-loop mode to track pseudorandom position reference inputs against a load emulated by a spring. Furthermore, the method is shown to remain effective even with control systems which are tolerant to leakage faults. Next, the application of wavelet transform to detect external leakage fault using both offline and online applications in hydraulic actuators is described. The method also examines the isolation of this fault from actuator internal leakage in a multiple-fault environment. The results show that wavelet transform is a fast and easily-implementable method for leakage detection in hydraulic actuators without any need to explicitly incorporate the model of actuator or leakage. Internal leakages as low as 0.124 lit/min, are shown to be detectable, for 80% of the times using structured input signal. For online application, internal leakages in the range of 0.2-0.25 lit/min can be identified. External leakages as low as 0.3 lit/min can be detected in all offline and online applications. Other methods such as observer based and Kalman filter methods, which require the model of the actuator or leakage fault, cannot report leakage detection of magnitudes as low as that reported in this work. The low leak rate detection and not requiring a model of the actuator or leakage make this method very attractive for industrial implementation.